def plot_results(self, nrnManager, saving_dir):
for i, var in enumerate(self.param['var_to_plot']):
secs = self.param['section_to_plot']
vecs_to_plot = self.build_vecs_to_plot(var,
secs,
self.manager.refs['VecRef'])
self.manager.plot_vecs(vecs_to_plot, figure_num=i)
if var == 'v':
plt.ylabel("Voltage [mV]")
plt.xlabel("Time [ms]")
plt.ylim(-90) # Setting the minimum limits
elif var == 'cai' or var == 'cali':
plt.xlabel("Time [ms]")
plt.ylabel("Concentration [mM]")
elif var == 'ica':
plt.xlabel("Time [ms]")
plt.ylabel("Current [nA]")
#
fig_file = 'plot_' + var
plt.savefig(os.path.join(saving_dir, fig_file))
if self.param['bio_on']:
from helpers.plotter import EcellPlotter
ecp = EcellPlotter()
x_start = self.param['t_equilibrium_ecell']
x_stop = x_start + self.param['tStop']/1e3
for stim_spine in self.param['stimulated_spines']:
spine = nrnManager.spines[stim_spine]
ecp.plot_timeCourses(spine.ecellMan.timeCourses, save=True,
dir=saving_dir, name=spine.id,
x_lims= [x_start, x_stop])
ecp.plot_weight(spine.ecellMan.timeCourses, dir=saving_dir)
def plot_results(self, nrnManager, saving_dir):
for i, var in enumerate(self.param['var_to_plot']):
secs = self.param['section_to_plot']
vecs_to_plot = self.build_vecs_to_plot(var, secs,
self.manager.refs['VecRef'])
self.manager.plot_vecs(vecs_to_plot, figure_num=i)
if var == 'v':
plt.ylabel("Voltage [mV]")
plt.xlabel("Time [ms]")
plt.ylim(-90) # Setting the minimum limits
elif var == 'cai' or var == 'cali':
plt.xlabel("Time [ms]")
plt.ylabel("Concentration [mM]")
elif var == 'ica':
plt.xlabel("Time [ms]")
plt.ylabel("Current [nA]")
#
fig_file = 'plot_' + var
plt.savefig(os.path.join(saving_dir, fig_file))
if self.param['bio_on']:
from helpers.plotter import EcellPlotter
ecp = EcellPlotter()
x_start = self.param['t_equilibrium_ecell']
x_stop = x_start + self.param['tStop'] / 1e3
for stim_spine in self.param['stimulated_spines']:
spine = nrnManager.spines[stim_spine]
ecp.plot_timeCourses(spine.ecellMan.timeCourses,
save=True,
dir=saving_dir,
name=spine.id,
x_lims=[x_start, x_stop])
ecp.plot_weight(spine.ecellMan.timeCourses, dir=saving_dir)
parameter_file = sys.argv[1]
param = parameters.ParameterSet(parameter_file)
## Setting the mat plotlib backend
import matplotlib
if param['interactive'] == False:
matplotlib.use('Agg')
print "Switching backend to Agg. Batch execution"
import matplotlib.pyplot as plt
from helpers.plotter import EcellPlotter
import helpers
loader = helpers.Loader()
# ecellManager = testChangeCalciumValue(interval, caValue)
if param['running_type'] == 'train':
ecellManager = testCalciumTrain(param['num_spikes'],
param['delay'],
param['biochemical_filename'])
ecp = EcellPlotter()
if param['interactive'] == False:
dir = loader.create_new_dir(prefix=os.getcwd())
loader.save(ecellManager.timeCourses, dir, "timeCourses")
ecp.plot_timeCourses(ecellManager.timeCourses, save=True, dir=dir)
ecp.plot_weight(ecellManager.timeCourses, dir=dir)
else:
ecp.plot_timeCourses(ecellManager.timeCourses)
ecp.plot_weight(ecellManager.timeCourses)
plt.show()
sys.exit()
parameter_file = sys.argv[1]
param = parameters.ParameterSet(parameter_file)
## Setting the mat plotlib backend
import matplotlib
if param['interactive'] == False:
matplotlib.use('Agg')
print "Switching backend to Agg. Batch execution"
import matplotlib.pyplot as plt
from helpers.plotter import EcellPlotter
import helpers
loader = helpers.Loader()
# ecellManager = testChangeCalciumValue(interval, caValue)
if param['running_type'] == 'train':
ecellManager = testCalciumTrain(param['num_spikes'], param['delay'],
param['biochemical_filename'])
ecp = EcellPlotter()
if param['interactive'] == False:
dir = loader.create_new_dir(prefix=os.getcwd())
loader.save(ecellManager.timeCourses, dir, "timeCourses")
ecp.plot_timeCourses(ecellManager.timeCourses, save=True, dir=dir)
ecp.plot_weight(ecellManager.timeCourses, dir=dir)
else:
ecp.plot_timeCourses(ecellManager.timeCourses)
ecp.plot_weight(ecellManager.timeCourses)
plt.show()
